Supplied Demand Technique for Power System Balance Reliability Calculations

2014 ◽  
Vol 960-961 ◽  
pp. 1512-1515 ◽  
Author(s):  
Vladislav Petrovich Oboskalov ◽  
Irina Lvovna Kirpikova ◽  
Stanislava Matugova ◽  
Sergey Aleksandrovich Gusev
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Solution ◽  
Power System ◽  
Probability Density ◽  
High Accuracy ◽  
Probability Density Functions ◽  
Density Functions ◽  
Power Imbalance

This paper presents an iterative power system balance reliability calculating technique ensuring better convergence and faster calculations. The technique is called "supplied demand". Nodal power imbalance is considered as the primary stochastic value under analysis. An analytical solution does not contain probability density functions. It results in faster calculations. Results were verified by Monte Carlo Simulation and showed high accuracy of the technique.

A New Procedure for Measuring High-Accuracy Probability Density Functions

2012 ◽  
Author(s):  
Takahiro J. Yamaguchi ◽  
Kunihiro Asada ◽  
Kiichi Niitsu ◽  
Mohamed Abbas ◽  
Satoshi Komatsu ◽  
...  
Keyword(s):  
Probability Density ◽  
High Accuracy ◽  
Probability Density Functions ◽  
Density Functions

Discontinuous Reinjection Probability Density functions in Type V Intermittency

2018 ◽  
Vol 13 (12) ◽  
Author(s):  
Sergio Elaskar ◽  
Ezequiel del Río
Keyword(s):  
Probability Density ◽  
Lower Boundary ◽  
Numerical Data ◽  
High Accuracy ◽  
Probability Density Functions ◽  
Density Functions ◽  
Exponential Functions ◽  
Characteristic Relation ◽  
Type V ◽  
Very High

This paper reports theoretical and numerical results about the reinjection process in type V intermittency. The M function methodology is applied to a simple mathematical model to evaluate the reinjection process through the reinjection probability density function (RPD), the probability density of laminar lengths, and the characteristic relation. We have found that the RPD can be a discontinuous function and it is a sum of exponential functions. The RPD shows two reinjection behaviors. Also, the probability density of laminar lengths has two different behaviors following the RPD function. The dependence of the RPD function and the probability density of laminar lengths with the reinjection mechanisms and the lower boundary of return are considered. On the other hand, we have obtained, for the analyzed map, that the characteristic relation verifies l¯≈ε−0.5. Finally, we highlight that the M function methodology is a suitable tool to analyze type V intermittency and there is a very high accuracy between the new theoretical equations and the numerical data.

scholarly journals Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

2021 ◽  
Vol 13 (12) ◽  
pp. 2307
Author(s):  
J. Javier Gorgoso-Varela ◽  
Rafael Alonso Ponce ◽  
Francisco Rodríguez-Puerta
Keyword(s):  
Probability Density ◽  
Linear Models ◽  
Pinus Halepensis ◽  
Beta Function ◽  
Probability Density Functions ◽  
Lidar Data ◽  
Density Functions ◽  
Minimum Diameter ◽  
Location Parameters ◽  
Diameter Distributions

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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